We are collecting a dataset on water quality to train a machine learning model for binary classification: determining whether water is safe for consumption (1) or not (0). This model will help with water treatment decisions and ensure compliance with quality standards. We applied different summarization and plotting methods to help us to understand our dataset, such as scatter, histogram and bar plot. Then, we applyed preprocess in our data using data cleaning, data transformation and feature selection.

#library:
#install.packages("caret")
#install.packages("glmnet")
#install.packages("Boruta")
#install.packages("mlbench")
#install.packages("randomForest")
library(outliers)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
library(mlbench)
library(caret)
Loading required package: ggplot2
Use suppressPackageStartupMessages() to eliminate package startup messages
Loading required package: lattice
library(glmnet)
Loading required package: Matrix
Loaded glmnet 4.1-8
library(Boruta)
library(ggplot2)
library(randomForest)
randomForest 4.7-1.1
Type rfNews() to see new features/changes/bug fixes.

Attaching package: ‘randomForest’

The following object is masked from ‘package:ggplot2’:

    margin

The following object is masked from ‘package:dplyr’:

    combine

The following object is masked from ‘package:outliers’:

    outlier
getwd()
[1] "/Users/mahayie/Documents/GitHub/DM1Project"
#setwd("/Users/mahayie/Desktop/326p")
#getwd()

water_potability = read.csv('Dataset/water_potability.csv')

View(water_potability)

str(water_potability)
'data.frame':   3276 obs. of  10 variables:
 $ ph             : num  NA 3.72 8.1 8.32 9.09 ...
 $ Hardness       : num  205 129 224 214 181 ...
 $ Solids         : num  20791 18630 19910 22018 17979 ...
 $ Chloramines    : num  7.3 6.64 9.28 8.06 6.55 ...
 $ Sulfate        : num  369 NA NA 357 310 ...
 $ Conductivity   : num  564 593 419 363 398 ...
 $ Organic_carbon : num  10.4 15.2 16.9 18.4 11.6 ...
 $ Trihalomethanes: num  87 56.3 66.4 100.3 32 ...
 $ Turbidity      : num  2.96 4.5 3.06 4.63 4.08 ...
 $ Potability     : int  0 0 0 0 0 0 0 0 0 0 ...
summary(water_potability)
       ph            Hardness          Solids         Chloramines        Sulfate       Conductivity  
 Min.   : 0.000   Min.   : 47.43   Min.   :  320.9   Min.   : 0.352   Min.   :129.0   Min.   :181.5  
 1st Qu.: 6.093   1st Qu.:176.85   1st Qu.:15666.7   1st Qu.: 6.127   1st Qu.:307.7   1st Qu.:365.7  
 Median : 7.037   Median :196.97   Median :20927.8   Median : 7.130   Median :333.1   Median :421.9  
 Mean   : 7.081   Mean   :196.37   Mean   :22014.1   Mean   : 7.122   Mean   :333.8   Mean   :426.2  
 3rd Qu.: 8.062   3rd Qu.:216.67   3rd Qu.:27332.8   3rd Qu.: 8.115   3rd Qu.:360.0   3rd Qu.:481.8  
 Max.   :14.000   Max.   :323.12   Max.   :61227.2   Max.   :13.127   Max.   :481.0   Max.   :753.3  
 NA's   :491                                                          NA's   :781                    
 Organic_carbon  Trihalomethanes     Turbidity       Potability    
 Min.   : 2.20   Min.   :  0.738   Min.   :1.450   Min.   :0.0000  
 1st Qu.:12.07   1st Qu.: 55.845   1st Qu.:3.440   1st Qu.:0.0000  
 Median :14.22   Median : 66.622   Median :3.955   Median :0.0000  
 Mean   :14.28   Mean   : 66.396   Mean   :3.967   Mean   :0.3901  
 3rd Qu.:16.56   3rd Qu.: 77.337   3rd Qu.:4.500   3rd Qu.:1.0000  
 Max.   :28.30   Max.   :124.000   Max.   :6.739   Max.   :1.0000  
                 NA's   :162                                       

Checking for missing values:

dim(water_potability)
[1] 3276   10
sum(is.na(water_potability))
[1] 1434

Remove rows with missing values

water_potability = na.omit(water_potability)
View(water_potability)

Description: The absence of data in certain variables or columns in a dataset is referred to as missing or null values due to various reasons. It can have a negative impact on the dataset’s efficiency and the information that can be taken from it later, so we checked to see whether our data had missing or null values and eliminated these rows to produce a more efficient dataset.

Standard deviation:

sd(water_potability$Turbidity)
[1] 0.7803462
sd(water_potability$Solids)
[1] 8642.24
sd(water_potability$Conductivity)
[1] 80.71257
sd(water_potability$Organic_carbon)
[1] 3.324959
sd(water_potability$ph)
[1] 1.573337

Mean:

mean(water_potability$Turbidity)
[1] 3.969729
mean(water_potability$Solids) 
[1] 21917.44
mean(water_potability$Conductivity) 
[1] 426.5264
mean(water_potability$Organic_carbon) 
[1] 14.35771
mean(water_potability$ph) 
[1] 7.08599

Median

median(water_potability$Turbidity)
[1] 3.968177
median(water_potability$Solids)
[1] 20933.51
median(water_potability$Conductivity)
[1] 423.4559
median(water_potability$Organic_carbon)
[1] 14.32202
median(water_potability$ph)
[1] 7.027297

Variance

var(water_potability$Turbidity)
[1] 0.6089401
var(water_potability$Solids)
[1] 74688309
var(water_potability$Conductivity)
[1] 6514.519
var(water_potability$Organic_carbon)
[1] 11.05535
var(water_potability$ph)
[1] 2.475388

Statistical Measures:

summary(water_potability$Conductivity)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  201.6   366.7   423.5   426.5   482.4   753.3 
summary(water_potability$Organic_carbon)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2.20   12.12   14.32   14.36   16.68   27.01 
summary(water_potability$Hardness)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  73.49  176.74  197.19  195.97  216.44  317.34 
summary(water_potability$Solids)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  320.9 15615.7 20933.5 21917.4 27182.6 56488.7 
summary(water_potability$Chloramines)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.391   6.139   7.144   7.134   8.110  13.127 
summary(water_potability$Potability)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.4033  1.0000  1.0000 
summary(water_potability$Sulfate)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  129.0   307.6   332.2   333.2   359.3   481.0 
summary(water_potability$Trihalomethanes)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  8.577  55.953  66.542  66.401  77.292 124.000 
summary(water_potability$Turbidity)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.450   3.443   3.968   3.970   4.514   6.495 
summary(water_potability$ph)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2275  6.0897  7.0273  7.0860  8.0530 14.0000 

Descriotion: With using minimum, maximum, mean, median laws it helps to provide an overview of the data’s key characteristics

outliers before removing outlier:

dim(water_potability)
[1] 2011   10
head(water_potability)

removing outliers:

summary(water_potability$ph)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2275  6.0897  7.0273  7.0860  8.0530 14.0000 
quartiles <- quantile(water_potability$ph, probs = c(.25, .75), na.rm = FALSE)
quartiles
     25%      75% 
6.089723 8.052969 
iqr <- IQR(water_potability$ph)
iqr
[1] 1.963245
lower <- quartiles[1] - 1.5*iqr
lower
     25% 
3.144855 
upper <- quartiles[2] + 1.5*iqr
upper
     75% 
10.99784 
boxplot(ph ~ Potability, data = water_potability)


repeat {
  out_val <- boxplot(water_potability$ph, ylab = 'ph')$out
  out_val
  out_rows <- which(water_potability$ph %in% c(out_val))
  out_rows
  
  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$ph)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.231   6.105   7.027   7.087   8.030  10.905 
#-------------------------------------------

-Hardness

summary(water_potability$Hardness)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  73.49  176.90  197.36  196.27  216.44  317.34 
quartiles <- quantile(water_potability$Hardness, probs = c(.25, .75), na.rm = FALSE)
quartiles
     25%      75% 
176.9031 216.4411 
iqr <- IQR(water_potability$Hardness)
iqr
[1] 39.53799
lower <- quartiles[1] - 1.5*iqr
lower
     25% 
117.5961 
upper <- quartiles[2] + 1.5*iqr
upper
     75% 
275.7481 
boxplot(Hardness ~ Potability, data = water_potability)


repeat {
  out_val <- boxplot(water_potability$Hardness, ylab = 'Hardness')$out
  out_val
  out_rows <- which(water_potability$Hardness %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Hardness)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  121.0   177.7   197.3   196.2   215.5   272.1 
#-------------------------------------------

-Solids

summary(water_potability$Solids)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  320.9 15704.5 20855.3 21840.2 27045.9 56488.7 
quartiles <- quantile(water_potability$Solids, probs = c(.25, .75), na.rm = FALSE)
quartiles
     25%      75% 
15704.48 27045.93 
iqr <- IQR(water_potability$Solids)
iqr
[1] 11341.45
lower <- quartiles[1] - 1.5*iqr
lower
     25% 
-1307.69 
upper <- quartiles[2] + 1.5*iqr
upper
    75% 
44058.1 
boxplot(Solids ~ Potability, data = water_potability)


repeat {
  out_val <- boxplot(water_potability$Solids, ylab = 'Solids')$out
  out_val
  out_rows <- which(water_potability$Solids %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Solids)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  320.9 15547.5 20518.7 21419.6 26734.7 43195.5 
#-------------------------------------------

-Chloramines

summary(water_potability$Chloramines)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.391   6.141   7.135   7.135   8.094  13.127 
quartiles <- quantile(water_potability$Chloramines, probs = c(.25, .75), na.rm = FALSE)
quartiles
     25%      75% 
6.141236 8.094323 
iqr <- IQR(water_potability$Chloramines)
iqr
[1] 1.953087
lower <- quartiles[1] - 1.5*iqr
lower
     25% 
3.211605 
upper <- quartiles[2] + 1.5*iqr
upper
     75% 
11.02395 
boxplot(Chloramines ~ Potability, data = water_potability)


repeat {
  out_val <- boxplot(water_potability$Chloramines, ylab = 'Chloramines')$out
  out_val
  out_rows <- which(water_potability$Chloramines %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Chloramines)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.352   6.181   7.137   7.136   8.076  10.897 
#-------------------------------------------

-Sulfate

summary(water_potability$Sulfate)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  187.2   308.2   332.6   333.4   358.3   481.0 
quartiles <- quantile(water_potability$Sulfate, probs = c(.25, .75), na.rm = FALSE)
quartiles
     25%      75% 
308.1884 358.3020 
iqr <- IQR(water_potability$Sulfate)
iqr
[1] 50.11358
lower <- quartiles[1] - 1.5*iqr
lower
     25% 
233.0181 
upper <- quartiles[2] + 1.5*iqr
upper
     75% 
433.4724 
boxplot(Sulfate ~ Potability, data = water_potability)


repeat {
  out_val <- boxplot(water_potability$Sulfate, ylab = 'Sulfate')$out
  out_val
  out_rows <- which(water_potability$Sulfate %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Sulfate)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  237.5   309.2   332.8   333.6   357.7   429.8 
#-------------------------------------------

-Conductivity

summary(water_potability$Conductivity)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  201.6   366.6   423.6   426.8   482.6   753.3 
quartiles <- quantile(water_potability$Conductivity, probs = c(.25, .75), na.rm = FALSE)
quartiles
     25%      75% 
366.5581 482.5983 
iqr <- IQR(water_potability$Conductivity)
iqr
[1] 116.0401
lower <- quartiles[1] - 1.5*iqr
lower
     25% 
192.4979 
upper <- quartiles[2] + 1.5*iqr
upper
     75% 
656.6585 
boxplot(Conductivity ~ Potability, data = water_potability)


repeat {
  out_val <- boxplot(water_potability$Conductivity, ylab = 'Conductivity')$out
  out_val
  out_rows <- which(water_potability$Conductivity %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Conductivity)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  201.6   366.4   423.1   426.0   481.9   652.5 
#-------------------------------------------

-Organic_carbon

summary(water_potability$Organic_carbon)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  4.372  12.184  14.351  14.417  16.788  27.007 
quartiles <- quantile(water_potability$Organic_carbon, probs = c(.25, .75), na.rm = FALSE)
quartiles
     25%      75% 
12.18447 16.78779 
iqr <- IQR(water_potability$Organic_carbon)
iqr
[1] 4.603315
lower <- quartiles[1] - 1.5*iqr
lower
     25% 
5.279502 
upper <- quartiles[2] + 1.5*iqr
upper
     75% 
23.69276 
boxplot(Organic_carbon ~ Potability, data = water_potability)


repeat {
  out_val <- boxplot(water_potability$Organic_carbon, ylab = 'Organic_carbon')$out
  out_val
  out_rows <- which(water_potability$Organic_carbon %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Organic_carbon)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  5.512  12.222  14.352  14.426  16.786  23.604 
#-------------------------------------------

-Trihalomethanes

summary(water_potability$Trihalomethanes)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  8.577  55.865  66.231  66.364  77.418 124.000 
quartiles <- quantile(water_potability$Trihalomethanes, probs = c(.25, .75), na.rm = FALSE)
quartiles
     25%      75% 
55.86494 77.41789 
iqr <- IQR(water_potability$Trihalomethanes)
iqr
[1] 21.55295
lower <- quartiles[1] - 1.5*iqr
lower
     25% 
23.53552 
upper <- quartiles[2] + 1.5*iqr
upper
     75% 
109.7473 
boxplot(Trihalomethanes ~ Potability, data = water_potability)


repeat {
  out_val <- boxplot(water_potability$Trihalomethanes, ylab = 'Trihalomethanes')$out
  out_val
  out_rows <- which(water_potability$Trihalomethanes %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Trihalomethanes)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  24.53   55.96   66.29   66.42   77.34  108.85 
#-------------------------------------------

-Turbidity

summary(water_potability$Turbidity)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.450   3.441   3.975   3.973   4.519   6.495 
quartiles <- quantile(water_potability$Turbidity, probs = c(.25, .75), na.rm = FALSE)
quartiles
     25%      75% 
3.440859 4.518751 
iqr <- IQR(water_potability$Turbidity)
iqr
[1] 1.077892
lower <- quartiles[1] - 1.5*iqr
lower
     25% 
1.824021 
upper <- quartiles[2] + 1.5*iqr
upper
     75% 
6.135588 
boxplot(Turbidity ~ Potability, data = water_potability)


repeat {
  out_val <- boxplot(water_potability$Turbidity, ylab = 'Turbidity')$out
  out_val
  out_rows <- which(water_potability$Turbidity %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}

summary(water_potability$Turbidity)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.873   3.443   3.974   3.972   4.512   6.084 

After removing outliers:

dim(water_potability)
[1] 1750   10
str(water_potability)
'data.frame':   1750 obs. of  10 variables:
 $ ph             : num  8.32 9.09 5.58 10.22 8.64 ...
 $ Hardness       : num  214 181 188 248 203 ...
 $ Solids         : num  22018 17979 28749 28750 13672 ...
 $ Chloramines    : num  8.06 6.55 7.54 7.51 4.56 ...
 $ Sulfate        : num  357 310 327 394 303 ...
 $ Conductivity   : num  363 398 280 284 475 ...
 $ Organic_carbon : num  18.4 11.6 8.4 13.8 12.4 ...
 $ Trihalomethanes: num  100.3 32 54.9 84.6 62.8 ...
 $ Turbidity      : num  4.63 4.08 2.56 2.67 4.4 ...
 $ Potability     : int  0 0 0 0 0 0 0 0 0 0 ...
 - attr(*, "na.action")= 'omit' Named int [1:1265] 1 2 3 9 12 14 15 17 19 21 ...
  ..- attr(*, "names")= chr [1:1265] "1" "2" "3" "9" ...
head(water_potability)

Description: Removing outliers from a dataset is critical for assuring the quality and reliability of statistical analysis and machine learning models. We found all outliers in the numerical attributes and subsequently eliminated the rows containing the outliers.

Charts

Histogram

hist(water_potability$ph)

hist(water_potability$Chloramines)

hist(water_potability$Hardness)

Bar Plot

water_potability$Potability %>% table() %>% barplot()


# add colors
bb <- water_potability$ph %>% table() %>% barplot( main='ph',col=c('pink'))

Pie chart

water_potability$Potability %>% table() %>% pie() # plot pie chart without percentages


tab <- water_potability$Potability %>% table()
precentages <- tab %>% prop.table() %>% round(3)*100
txt <- paste0(names(c("Not Potable","Potable")),'\n',precentages,'%')
pie(tab,labels = c("Not potabale","Potabale"))

Scatter Plot

with(water_potability, plot(Turbidity, ph, col = Potability, pch = as.numeric(Potability)))

Description: -Histogram: The histogram shows the frequency of ph in the dataset; we noted that the majority of values fall within the usual range, which is about between 6 and 8, but it also shows several outliers. -Scatter plot: This scatter demonstrates the correlation and proportionality between the two qualities, allowing us to establish whether or not turbidity and pH are connected. -Bar Plot the bar plot represent how ph levels affect water portability in the dataset it indicates that ph level above 10 is not portibal and humans cant consume it

Remove Redundant Features:

correlation_matrix <- cor(water_potability[,1:9])
high_correlation_features <- findCorrelation(correlation_matrix, cutoff = 0.5)
print(high_correlation_features)
integer(0)
heatmap(correlation_matrix)

Description: This will find the correlation between the features and represent it in heat map

Feature selection

Rank Features By Importance:

#train random forest model and calculate feature importance
rf = randomForest(x= water_potability[,1:9],y= water_potability[,10])
var_imp <- varImp(rf, scale = FALSE)
#sort the score in decreasing order
var_imp_df <- data.frame(cbind(variable = rownames(var_imp), score = var_imp[,1]))
var_imp_df$score <- as.double(var_imp_df$score)
var_imp_df[order(var_imp_df$score,decreasing = TRUE),]

ggplot(var_imp_df, aes(x=reorder(variable, score), y=score)) + 
  geom_point() +
  geom_segment(aes(x=variable,xend=variable,y=0,yend=score)) +
  ylab("IncNodePurity") +
  xlab("Variable Name") +
  coord_flip()

Recursive Feature elimination:

control <- rfeControl(functions=rfFuncs, method="cv",number=10)
rf <- trainControl(method = "cv", number = 10, verboseIter = FALSE)
# run the RFE algorithm
rfe_model <- rfe(x= water_potability[,1:9],y= water_potability[,10], sizes=c(1:9), rfeControl=control)
# summarize the results
print(rfe_model)

Recursive feature selection

Outer resampling method: Cross-Validated (10 fold) 

Resampling performance over subset size:

The top 5 variables (out of 5):
   Sulfate, ph, Hardness, Solids, Chloramines
# list the chosen features
predictors(rfe_model)
[1] "Sulfate"     "ph"          "Hardness"    "Solids"      "Chloramines"
# plot the results
plot(rfe_model, type=c("g", "o"))

Description: ranking features by importance is a technique used to identify the most influential variables in a dataset for predicting a target variable. This process helps in understanding which features have the most impact on the model’s performance. By ranking features by importance.

removing redundant features refers to the process of eliminating variables or features from a dataset that do not provide any additional or unique information.

Data transformation

Normlization

normalize=function(x){return ((x-min(x))/(max(x)))}
w = water_potability

water_potability$Sulfate=normalize(water_potability$Sulfate)
water_potability$Conductivity=normalize(water_potability$Conductivity)
water_potability$Turbidity=normalize(water_potability$Turbidity)
water_potability$ph=normalize(water_potability$ph)
water_potability$Chloramines=normalize(water_potability$Chloramines)
water_potability$Solids=normalize(water_potability$Solids)
water_potability$Trihalomethanes=normalize(water_potability$Trihalomethanes)
water_potability$Organic_carbon=normalize(water_potability$Organic_carbon)
water_potability$Hardness=water_potability$Hardness/1000

water_potability$Hardness<-normalize(water_potability$Hardness)
print(water_potability)

Description: Normalization refers to the process of scaling variables to have a common range. It helps in comparing variables with different scales. In the solids attribute will create critical challenges since of the huge and diverted values(min=320,9 max=43195.5) so we normalized the solids to make values smaller and more reasonable. Also we normalized all the scaled attributes:Sulfate,Conductivity,Organic_corbon, Trihalomerhanes, Turbidity,ph,Chloramines.

Discretization:

w$Trihalomethanes= cut(w$Trihalomethanes, breaks = seq(0,125,by=25),right=FALSE)
w$Solids= cut(w$Solids, breaks = seq(0,50000,by=10000),right=FALSE)
w$Organic_carbon= cut(w$Organic_carbon, breaks = seq(0,25,by=5),right=FALSE)
print(w)

Description: Discretization is the process of transforming continuous variables into discrete or categorical variables. It’s can be useful for analyzing data that has a large number of unique values or when you want to simplify the data.So In Trihalomethanes we intervals by dividing the values by 25 to have a labels with equal width : (0,25],(25,50],(50,75],(75,100],(100,125].

Encoding encoding is the process of converting characters or strings into a specific encoding format. Since we don’t have a Nominal attribute in our database we couldn’t implement it.

---
title: "R Notebook"
output: html_notebook
--- 


We are collecting a dataset on water quality to train a machine learning model
for binary classification: determining whether water is safe for consumption (1)
or not (0). This model will help with water treatment decisions and ensure
compliance with quality standards.
We applied different summarization and plotting methods to help us to understand
our dataset, such as scatter, histogram and bar plot. Then, we applyed
preprocess in our data using data cleaning, data transformation 
and feature selection.

```{r}
#library:
#install.packages("caret")
#install.packages("glmnet")
#install.packages("Boruta")
#install.packages("mlbench")
#install.packages("randomForest")
library(outliers)
library(dplyr)
library(mlbench)
library(caret)
library(glmnet)
library(Boruta)
library(ggplot2)
library(randomForest)

getwd()
#setwd("/Users/mahayie/Desktop/326p")
#getwd()

water_potability = read.csv('Dataset/water_potability.csv')

View(water_potability)

str(water_potability)
summary(water_potability)
```

Checking for missing values:
```{r}
dim(water_potability)
sum(is.na(water_potability))
```
Remove rows with missing values
```{r}
water_potability = na.omit(water_potability)
View(water_potability)
```

Description:
The absence of data in certain variables or columns in a dataset is referred to as missing or null values due to various reasons.  It can have a negative impact on the dataset's efficiency and the information that can be taken from it later, so we checked to see whether our data had missing or null values and eliminated these rows to produce a more efficient dataset.


Standard deviation:
```{r}
sd(water_potability$Turbidity)
sd(water_potability$Solids)
sd(water_potability$Conductivity)
sd(water_potability$Organic_carbon)
sd(water_potability$ph)
```

Mean:
```{r}
mean(water_potability$Turbidity)
mean(water_potability$Solids) 
mean(water_potability$Conductivity) 
mean(water_potability$Organic_carbon) 
mean(water_potability$ph) 
```

Median
```{r}
median(water_potability$Turbidity)
median(water_potability$Solids)
median(water_potability$Conductivity)
median(water_potability$Organic_carbon)
median(water_potability$ph)
```

Variance
```{r}
var(water_potability$Turbidity)
var(water_potability$Solids)
var(water_potability$Conductivity)
var(water_potability$Organic_carbon)
var(water_potability$ph)
```

Statistical Measures:
```{r}
summary(water_potability$Conductivity)
summary(water_potability$Organic_carbon)
summary(water_potability$Hardness)
summary(water_potability$Solids)
summary(water_potability$Chloramines)
summary(water_potability$Potability)
summary(water_potability$Sulfate)
summary(water_potability$Trihalomethanes)
summary(water_potability$Turbidity)
summary(water_potability$ph)
```

Descriotion:
With using minimum, maximum, mean, median laws it helps to provide an overview of the data's key characteristics


outliers
before removing outlier:
```{r}
dim(water_potability)
head(water_potability)
```

removing outliers:

- ph
```{r}
summary(water_potability$ph)
quartiles <- quantile(water_potability$ph, probs = c(.25, .75), na.rm = FALSE)
quartiles
iqr <- IQR(water_potability$ph)
iqr
lower <- quartiles[1] - 1.5*iqr
lower
upper <- quartiles[2] + 1.5*iqr
upper

boxplot(ph ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$ph, ylab = 'ph')$out
  out_val
  out_rows <- which(water_potability$ph %in% c(out_val))
  out_rows
  
  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}
summary(water_potability$ph)

#-------------------------------------------
```

-Hardness
```{r}
summary(water_potability$Hardness)
quartiles <- quantile(water_potability$Hardness, probs = c(.25, .75), na.rm = FALSE)
quartiles
iqr <- IQR(water_potability$Hardness)
iqr
lower <- quartiles[1] - 1.5*iqr
lower
upper <- quartiles[2] + 1.5*iqr
upper

boxplot(Hardness ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Hardness, ylab = 'Hardness')$out
  out_val
  out_rows <- which(water_potability$Hardness %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}
summary(water_potability$Hardness)

#-------------------------------------------
```

-Solids
```{r}
summary(water_potability$Solids)
quartiles <- quantile(water_potability$Solids, probs = c(.25, .75), na.rm = FALSE)
quartiles
iqr <- IQR(water_potability$Solids)
iqr
lower <- quartiles[1] - 1.5*iqr
lower
upper <- quartiles[2] + 1.5*iqr
upper

boxplot(Solids ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Solids, ylab = 'Solids')$out
  out_val
  out_rows <- which(water_potability$Solids %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}
summary(water_potability$Solids)

#-------------------------------------------
```

-Chloramines
```{r}
summary(water_potability$Chloramines)
quartiles <- quantile(water_potability$Chloramines, probs = c(.25, .75), na.rm = FALSE)
quartiles
iqr <- IQR(water_potability$Chloramines)
iqr
lower <- quartiles[1] - 1.5*iqr
lower
upper <- quartiles[2] + 1.5*iqr
upper

boxplot(Chloramines ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Chloramines, ylab = 'Chloramines')$out
  out_val
  out_rows <- which(water_potability$Chloramines %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}
summary(water_potability$Chloramines)

#-------------------------------------------

```

-Sulfate
```{r}
summary(water_potability$Sulfate)
quartiles <- quantile(water_potability$Sulfate, probs = c(.25, .75), na.rm = FALSE)
quartiles
iqr <- IQR(water_potability$Sulfate)
iqr
lower <- quartiles[1] - 1.5*iqr
lower
upper <- quartiles[2] + 1.5*iqr
upper

boxplot(Sulfate ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Sulfate, ylab = 'Sulfate')$out
  out_val
  out_rows <- which(water_potability$Sulfate %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}
summary(water_potability$Sulfate)

#-------------------------------------------

```

-Conductivity
```{r}
summary(water_potability$Conductivity)
quartiles <- quantile(water_potability$Conductivity, probs = c(.25, .75), na.rm = FALSE)
quartiles
iqr <- IQR(water_potability$Conductivity)
iqr
lower <- quartiles[1] - 1.5*iqr
lower
upper <- quartiles[2] + 1.5*iqr
upper

boxplot(Conductivity ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Conductivity, ylab = 'Conductivity')$out
  out_val
  out_rows <- which(water_potability$Conductivity %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}
summary(water_potability$Conductivity)

#-------------------------------------------
```

-Organic_carbon
```{r}
summary(water_potability$Organic_carbon)
quartiles <- quantile(water_potability$Organic_carbon, probs = c(.25, .75), na.rm = FALSE)
quartiles
iqr <- IQR(water_potability$Organic_carbon)
iqr
lower <- quartiles[1] - 1.5*iqr
lower
upper <- quartiles[2] + 1.5*iqr
upper

boxplot(Organic_carbon ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Organic_carbon, ylab = 'Organic_carbon')$out
  out_val
  out_rows <- which(water_potability$Organic_carbon %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}
summary(water_potability$Organic_carbon)

#-------------------------------------------
```

-Trihalomethanes
```{r}
summary(water_potability$Trihalomethanes)
quartiles <- quantile(water_potability$Trihalomethanes, probs = c(.25, .75), na.rm = FALSE)
quartiles
iqr <- IQR(water_potability$Trihalomethanes)
iqr
lower <- quartiles[1] - 1.5*iqr
lower
upper <- quartiles[2] + 1.5*iqr
upper

boxplot(Trihalomethanes ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Trihalomethanes, ylab = 'Trihalomethanes')$out
  out_val
  out_rows <- which(water_potability$Trihalomethanes %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}
summary(water_potability$Trihalomethanes)

#-------------------------------------------
```

-Turbidity
```{r}
summary(water_potability$Turbidity)
quartiles <- quantile(water_potability$Turbidity, probs = c(.25, .75), na.rm = FALSE)
quartiles
iqr <- IQR(water_potability$Turbidity)
iqr
lower <- quartiles[1] - 1.5*iqr
lower
upper <- quartiles[2] + 1.5*iqr
upper

boxplot(Turbidity ~ Potability, data = water_potability)

repeat {
  out_val <- boxplot(water_potability$Turbidity, ylab = 'Turbidity')$out
  out_val
  out_rows <- which(water_potability$Turbidity %in% c(out_val))
  out_rows

  if(sum(out_rows) > 0) water_potability <- water_potability[-out_rows,]
  else {break}
}
summary(water_potability$Turbidity)
```


After removing outliers:
```{r}
dim(water_potability)
str(water_potability)
head(water_potability)
```

Description:
Removing outliers from a dataset is critical for assuring the quality and reliability of statistical analysis and machine learning models. We found all outliers in the numerical attributes and subsequently eliminated the rows containing the outliers. 



Charts

Histogram
```{r}
hist(water_potability$ph)
hist(water_potability$Chloramines)
hist(water_potability$Hardness)
```

Bar Plot
```{r}
water_potability$Potability %>% table() %>% barplot()

# add colors
bb <- water_potability$ph %>% table() %>% barplot( main='ph',col=c('pink'))

```

Pie chart
```{r}
water_potability$Potability %>% table() %>% pie() # plot pie chart without percentages

tab <- water_potability$Potability %>% table()
precentages <- tab %>% prop.table() %>% round(3)*100
txt <- paste0(names(c("Not Potable","Potable")),'\n',precentages,'%')
pie(tab,labels = c("Not potabale","Potabale"))
```

Scatter Plot
```{r}
with(water_potability, plot(Turbidity, ph, col = Potability, pch = as.numeric(Potability)))
```
 
Description:
-Histogram:
  The histogram shows the frequency of ph in the dataset; we noted that the majority of values fall    within the usual range, which is about between 6 and 8, but it also shows several outliers.
-Scatter plot:
  This scatter demonstrates the correlation and proportionality between the two qualities, allowing    us to establish whether or not turbidity and pH are connected.
-Bar Plot
  the bar plot represent how ph levels affect water portability in the dataset it indicates that ph    level above 10 is not portibal and humans cant consume it 



Remove Redundant Features:
```{r}
correlation_matrix <- cor(water_potability[,1:9])
high_correlation_features <- findCorrelation(correlation_matrix, cutoff = 0.5)
print(high_correlation_features)
heatmap(correlation_matrix)
```
Description:
This will find the correlation between the features and represent it in heat map



Feature selection

Rank Features By Importance:
```{r, warning=FALSE}
#train random forest model and calculate feature importance
rf = randomForest(x= water_potability[,1:9],y= water_potability[,10])
var_imp <- varImp(rf, scale = FALSE)
#sort the score in decreasing order
var_imp_df <- data.frame(cbind(variable = rownames(var_imp), score = var_imp[,1]))
var_imp_df$score <- as.double(var_imp_df$score)
var_imp_df[order(var_imp_df$score,decreasing = TRUE),]

ggplot(var_imp_df, aes(x=reorder(variable, score), y=score)) + 
  geom_point() +
  geom_segment(aes(x=variable,xend=variable,y=0,yend=score)) +
  ylab("IncNodePurity") +
  xlab("Variable Name") +
  coord_flip()
```

Recursive Feature elimination:
```{r, warning=FALSE}
control <- rfeControl(functions=rfFuncs, method="cv",number=10)
rf <- trainControl(method = "cv", number = 10, verboseIter = FALSE)
# run the RFE algorithm
rfe_model <- rfe(x= water_potability[,1:9],y= water_potability[,10], sizes=c(1:9), rfeControl=control)
# summarize the results
print(rfe_model)
# list the chosen features
predictors(rfe_model)
# plot the results
plot(rfe_model, type=c("g", "o"))
```
Description:
ranking features by importance is a technique used to identify the most influential variables in a dataset for predicting a target variable. This process helps in understanding which features have the most impact on the model's performance. By ranking features by importance.

removing redundant features refers to the process of eliminating variables or features from a dataset that do not provide any additional or unique information.



Data transformation

Normlization
```{r}
normalize=function(x){return ((x-min(x))/(max(x)))}
w = water_potability

water_potability$Sulfate=normalize(water_potability$Sulfate)
water_potability$Conductivity=normalize(water_potability$Conductivity)
water_potability$Turbidity=normalize(water_potability$Turbidity)
water_potability$ph=normalize(water_potability$ph)
water_potability$Chloramines=normalize(water_potability$Chloramines)
water_potability$Solids=normalize(water_potability$Solids)
water_potability$Trihalomethanes=normalize(water_potability$Trihalomethanes)
water_potability$Organic_carbon=normalize(water_potability$Organic_carbon)
water_potability$Hardness=water_potability$Hardness/1000

water_potability$Hardness<-normalize(water_potability$Hardness)
print(water_potability)
```
Description:
Normalization refers to the process of scaling variables to have a common range. It helps in comparing variables with different scales. In the solids attribute will create critical challenges since of the huge and diverted values(min=320,9 max=43195.5) so we normalized the solids to make values smaller and more reasonable. Also we normalized all the scaled attributes:Sulfate,Conductivity,Organic_corbon, Trihalomerhanes, Turbidity,ph,Chloramines.



Discretization:
```{r}
w$Trihalomethanes= cut(w$Trihalomethanes, breaks = seq(0,125,by=25),right=FALSE)
w$Solids= cut(w$Solids, breaks = seq(0,50000,by=10000),right=FALSE)
w$Organic_carbon= cut(w$Organic_carbon, breaks = seq(0,25,by=5),right=FALSE)
print(w)
```
Description:
Discretization is the process of transforming continuous variables into discrete or categorical variables. It’s can be useful for analyzing data that has a large number of unique values or when you want to simplify the data.So In Trihalomethanes we intervals by dividing the values by 25 to have a labels with equal width : (0,25],(25,50],(50,75],(75,100],(100,125].


Encoding
encoding is the process of converting characters or strings into a specific encoding format. Since we don’t have a Nominal  attribute in our database we couldn’t implement it.




